A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

Mourikis, Anastasios I.; Roumeliotis, Stergios I.

doi:10.1109/robot.2007.364024

Cited by 1,376 publications

(1,134 citation statements)

References 23 publications

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“…However, we do not store OFs in the map. Instead, all OFs are processed and marginalized on-the-fly using the MSC-KF approach [25] (see Sect. 3.2).…”

Section: System State and Propagation Modelmentioning

confidence: 99%

“…Finally, for OFs, we employ the MSC-KF approach [25] to impose a pose update constraining all the views from which the feature was seen. To accomplish this, we utilize stochastic cloning [29] over a window of m camera poses.…”

Section: Measurement Update Modelmentioning

confidence: 99%

“…The first block column of (25) requires that H f = −H p I . Hence, we rewrite the second block column of (25) as…”

Section: Modification Of Hmentioning

confidence: 99%

See 2 more Smart Citations

Towards Consistent Vision-Aided Inertial Navigation

Hesch

Kottas

Bowman

et al. 2013

Springer Tracts in Advanced Robotics

Self Cite

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In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS) from a standpoint of system observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, resulting in smaller uncertainties, larger estimation errors, and possibly even divergence. We develop an Observability-Constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. Our analysis, along with the proposed method for reducing inconsistency, are extensively validated with simulation trials and real-world experiments.

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“…However, we do not store OFs in the map. Instead, all OFs are processed and marginalized on-the-fly using the MSC-KF approach [25] (see Sect. 3.2).…”

Section: System State and Propagation Modelmentioning

confidence: 99%

Section: Measurement Update Modelmentioning

confidence: 99%

See 1 more Smart Citation

Towards Consistent Vision-Aided Inertial Navigation

Hesch

Kottas

Bowman

et al. 2013

Springer Tracts in Advanced Robotics

Self Cite

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show abstract

“…Visual-inertial odometry with a translational error in the range of 1% of the distance traveled (similar to [29]) was used to construct the maps. Keypoints were detected using a Difference of Gaussians (DoG) keypoint detector.…”

Section: Methodsmentioning

confidence: 99%

Will It Last? Learning Stable Features for Long-Term Visual Localization

Dymczyk

Stumm

Nieto

et al. 2016

2016 Fourth International Conference on 3D Vision (3DV)

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Figure 1: An overview of the proposed algorithm: A classifier is used to decide whether a particular visual feature is expected to be persistent or not. Our method uses full image information as input and helps to maintain compact stable-over-time maps that can be used for life-long localization. AbstractAn increasing number of simultaneous localization and mapping (SLAM) systems are using appearance-based localization to improve the quality of pose estimates. However, with the growing time-spans and size of the areas we want to cover, appearance-based maps are often becoming too large to handle and are consisting of features that are not always reliable for localization purposes. This paper presents a method for selecting map features that are persistent over time and thus suited for long-term localization. Our methodology relies on a CNN classifier based on image patches and depth maps for recognizing which features are suitable for life-long matchability. Thus, the classifier not only considers the appearance of a feature but also takes into account its expected lifetime. As a result, our feature selection approach produces more compact maps with a high fraction of temporally-stable features compared to the current state-of-the-art, while rejecting unstable features that typically harm localization. Our approach is validated on indoor and outdoor datasets, that span over a period of several months.

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“…The rich representation of a scene captured in an image, together with the accurate short-term estimates by gyroscopes and accelerometers present in a typical IMU have been acknowledged to complement each other, with great uses in airborne [6,20] and automotive [14] navigation. Moreover, with the availability of these sensors in most smart phones, there is great interest and research activity in effective solutions to visual-inertial SLAM.…”

Section: Introductionmentioning

confidence: 99%

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

Leutenegger¹,

Furgale²,

Rabaud³

et al. 2013

Robotics: Science and Systems IX

339

248

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Abstract-The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non-linear optimization approaches for tasks such as visual Simultaneous Localization And Mapping (SLAM), following the discovery that this comes with significant advantages in quality of performance and computational complexity. Following this trend, we present a novel approach to tightly integrate visual measurements with readings from an Inertial Measurement Unit (IMU) in SLAM. An IMU error term is integrated with the landmark reprojection error in a fully probabilistic manner, resulting to a joint non-linear cost function to be optimized. Employing the powerful concept of 'keyframes' we partially marginalize old states to maintain a bounded-sized optimization window, ensuring real-time operation. Comparing against both vision-only and loosely-coupled visual-inertial algorithms, our experiments confirm the benefits of tight fusion in terms of accuracy and robustness.

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A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

Cited by 1,376 publications

References 23 publications

Towards Consistent Vision-Aided Inertial Navigation

Towards Consistent Vision-Aided Inertial Navigation

Will It Last? Learning Stable Features for Long-Term Visual Localization

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

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